Reasoning models on the RTX 5060 Ti 16GB have a different cost profile than standard LLMs on our hosting. Thinking-trace inflation changes the API-vs-dedicated calculation significantly.
Contents
- Token multiplier effect
- Throughput implications
- Monthly capacity
- API cost comparison
- Why self-hosting wins here
Token Multiplier
R1 distill models emit 2-5x more output tokens than non-reasoning models for the same final answer. They think in plain text before responding.
A typical math problem:
- Non-reasoning 7B: 80 output tokens
- R1 Distill 7B: 700 output tokens (mostly thinking) + 80 final answer
Token inflation factor: ~10x for reasoning workloads. This matters for API pricing which charges per-token.
Throughput
R1 Distill Qwen 7B FP8 on 5060 Ti:
- Decode rate: ~95 t/s batch 1
- Per-request duration: 8-10 seconds typical (700 tokens at 95 t/s)
- Concurrency capped by long request duration rather than decode speed
Monthly Capacity
At 50% utilisation:
- Output tokens (including thinking): ~450-550M/month
- Completed reasoning queries: ~50-80k/month
API Cost Comparison
DeepSeek API for R1-class reasoning (reasoning models priced higher due to thinking tokens):
- Input: ~$2/M
- Output: ~$8/M (reasoning premium)
- Your traffic equivalent: ~$3,600/month for 50k queries
Compare to non-reasoning API at the same task:
- Non-reasoning model would cost ~$300 for the same answers
- Reasoning premium: ~10x
Self-Host Wins Here
Because reasoning models inflate output tokens, API costs balloon. Self-hosted dedicated has fixed monthly cost regardless of thinking length. The break-even comes dramatically earlier than for non-reasoning models:
- Non-reasoning LLM: dedicated wins at ~35-50% utilisation
- Reasoning model: dedicated wins at ~10-15% utilisation
For any reasoning workload beyond experimentation, self-hosting pays back fast.
Reasoning at Mid-Tier Cost
R1 distilled into 7B on Blackwell 16GB. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: deployment guide, DeepSeek distill family.
